Scenario Tree and Adaptive Decision Making on Optimal Type and Timing for Intervention and Social-economic Activity Changes to Manage the COVID-19 Pandemic

Authors

  • Kyeongah Nah
  • Shengyuan Chen
  • Yanyu Xiao
  • Biao Tang
  • Nicola Bragazzi
  • Jane Heffernan
  • Ali Asgary
  • Nicholas Ogden
  • Jianhong Wu

DOI:

https://doi.org/10.29020/nybg.ejpam.v13i3.3792

Keywords:

Scenario tree, COVID-19 social distancing, lockdown exit strategy, re-opening, transmission dynamics model, stochastic optimization

Abstract

We introduce a novel approach to inform the re-opening plan followed by a postpandemic lockdown by integrating a stochastic optimization technique with a disease transmission model. We assess Ontarios re-opening plans as a case-study. Taking into account the uncertainties in contact rates during different re-opening phases, we find the optimal timing for the upcoming re-opening phase that maximizes the relaxation of social contacts under uncertainties, while not overwhelming the health system capacity before the arrival of effective therapeutics or vaccines.

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How to Cite

Nah, K., Chen, S., Xiao, Y., Tang, B., Bragazzi, N., Heffernan, J., Asgary, A., Ogden, N., & Wu, J. (2020). Scenario Tree and Adaptive Decision Making on Optimal Type and Timing for Intervention and Social-economic Activity Changes to Manage the COVID-19 Pandemic. European Journal of Pure and Applied Mathematics, 13(3), 710–729. https://doi.org/10.29020/nybg.ejpam.v13i3.3792